Advanced Intelligent Computing Theories and Applications. by Gang Zhou, Ling Wang, Ye Xu, Shengyao Wang (auth.),

By Gang Zhou, Ling Wang, Ye Xu, Shengyao Wang (auth.), De-Shuang Huang, Yong Gan, Phalguni Gupta, M. Michael Gromiha (eds.)

This booklet constitutes the completely refereed post-conference lawsuits of the seventh overseas convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The ninety four revised complete papers offered have been rigorously reviewed and chosen from 832 submissions. The papers are equipped in topical sections on clever computing in scheduling; neighborhood characteristic descriptors for picture processing and popularity; combinatorial and numerical optimization; computer studying thought and techniques; clever regulate and automation; wisdom representation/reasoning and professional structures; clever computing in trend acceptance; clever computing in photo processing; clever computing in laptop imaginative and prescient; biometrics with functions to person security/forensic sciences; modeling, concept, and purposes of confident platforms; sparse manifold studying tools and functions; advances in clever details processing.

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Extra resources for Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, Revised Selected Papers

Example text

Pareto optimal set is the collection of all Pareto optimal solutions and their corresponding images are called the Pareto optimal frontier. 3 Evolutionary Mechanism of Mopsocd_BL (1) Learning mechanism of Baldwinian An advantage of Baldwinian learning is that it can flatten out the fitness landscape around the optimal regions and thus enlarges the basin of allocated around the optimal regions. The enlargement of the basin can improve the evolution process and the overall effect of Baldwinian learning is that it can help improve the convergence efficiency and find the global optimum [7], the learning process is as follows: Fig.

For the solution of the TSP, a number of metaheuristic algorithms has been applied such as Simulated Annealing [4,5], Tabu Search[6], Genetic Algorithms [7,8,9], Variable Neighborhood Search [10], Iterated Local Search [11], Ant Colony Optimization[12], Particle Swarm Optimization [13] and Memetic Algorithm [14]. The GuoTao algorithm based on the Inver-over operator[7] is one of the best algorithms for solving the TSP in the evolution computational algorithms. However, the solution quality of the algorithm decreases with the problem size.

2 Paper Preparation Production period and resource allocation are two important objectives of FSP. Shortening the production period can improve productivity and increase flexibility of the system. Reducing idle time of equipments can avoid the waste of resource, and improve service life of equipments. Therefore, the objectives of scheduling can be described as follows [6]: (1) Minimize the production period. n (1) Minmize ( Max (Cis )) i=1 Where Cis represents the sth operation (the last operation) of job i.

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